A Robust Optimization Method for Systems With Significant Nonlinear Effects
Abstract This paper describes a robust optimization methodology for design involving either complex simulations or actual experiments. The proposed procedure optimizes the worst case response that consists of a weighted sum of expected mean and response variance. The estimation scheme for expected mean and variance adopts the modified 3-point Gauss quadrature integration to assure superior accuracy for systems with significant nonlinear effects. We apply the proposed method to the robust design of geometric parameters of heat treated parts to minimize the cost of post heat treatment operations. The paper investigates the major factors influencing geometric distortions due to heat treatment and the rules of thumb in design. The study focuses on relating dimensional distortion to the design of part geometry. To illustrate the utility of the proposed method, we present the formulation of a case study on allocation of dimensions of preheat treated (green) shafts to minimize the cost of post heat treatment operations. The final result is not presented yet pending the completion of further experiments.